The national and provincial supervision and sampling data of disposable abdominal trocars from 2018 to 2023 were systematically analyzed. This study revealed practical issues existing in the quality inspection process of the products, including inconsistent implementation of standards, non-uniform interpretation of testing methods, and insufficient implementation of ex-factory quality control. The main unqualified items identified in the supervision and sampling as well as their causes were elaborated on, and the disconnect between the requirements of inspection standards and actual operations was analyzed. From a full-chain perspective covering pre-market evaluation, in-process testing, and post-market supervision, specific pathways and supporting improvement suggestions for strengthening the medical device quality assurance system were proposed. This research not only provides empirical evidence for improving the quality standards and enhancing the regulatory efficiency of trocar products but also plays a positive role in promoting the standardized development of the entire medical device industry and ensuring the safety of clinical device use.

Objective To design a process equipment for improving the anchoring performance of spiral barbed suture tissues, in order to solve the problem of insufficient anchoring performance of existing spiral barbed suture tissues. Methods By using self-designed process equipment, the spiral barbed suture was treated through the synergistic effect of process parameters such as heating temperature, flipping angle and moving distance. The morphological characteristics of the barbs were observed by an optical microscope. The pull-out strength test and breaking strength test were conducted by a material testing machine at a specific tensile speed and test gauge length, and the in vitro tissue suture test was carried out on fresh pigskin tissue. The influence of different process parameters on the performance of the barbs was systematically evaluated. Results Under the combination of process parameters such as a heating temperature of 60 ℃, a flipping angle of 40°, and a translational distance of 1 mm (Group B), the barbs presented the best morphological characteristics. Their pull-out strength and the suture strength of isolated tissues were significantly improved compared with the untreated group (Group A) and group C, and the differences were statistically significant (P<0.05). Moreover, compared with groups A, C and D, there was no statistically significant difference in the fracture strength of the suture body in group B (P>0.05), while compared with group E, the difference was statistically significant (P<0.05). Conclusion This study provides important technical support for improving the safety and efficacy of the clinical application of spiral barbed suture, and offers a theoretical basis for the process optimization of spiral barbed suture.

Objective To develop a drug outer packaging retrieval model and integrate it into the drug dispensing system of the hospital outpatient pharmacy, in order to optimize the outpatient drug dispensing business process and improve the work efficiency and safety of the drug dispensing link. Methods A Feature extraction method based on the fusion of scale-invariant feature transform (SIFT) and speeded up robust features (SURF) was proposed. The drug outer packaging image retrieval algorithm combining vector of locally aggregated descriptors (VLAD) feature coding was used to train the collected outer packaging images of commonly used outpatient drugs. The running time of key steps, the prediction probability vector and the prediction result were taken as the evaluation indicators of this model. An interactive visual control was designed using Windows Form, and the algorithm was directly invoked by pharmacists through the operator interface. Results The visual dictionary model based on feature fusion took less time in generating the visual dictionary, VLAD encoding and the total training time compared with the visual dictionary model based on SIFT. Moreover, it has better prediction performance, with an accuracy of up to 99.02% and an average probability value of 94.77% for a single sample output. The outpatient pharmacy dispensing system optimized by the algorithm in this study has significantly improved in terms of waiting time for dispensing and the number of dispensing errors compared with that before optimization, and the differences were statistically significant (P<0.05). Conclusion Integrating the algorithm of feature fusion of SIFT and SURF and feature coding with VLAD into the dispensing system of the outpatient pharmacy can accurately retrieve the names of drug outer packaging, play an auxiliary role in the dispensing process of pharmacists, improve work efficiency, and reduce a series of medical disputes caused by dispensing errors.

Objective To address the issue of early diagnosis of atrial fibrillation, especially paroxysmal atrial fibrillation, and in view of the limitations of the current ambulatory electrocardiogram method that relies on electrode contact for atrial fibrillation detection, to study the non-contact detection and early identification technology of atrial fibrillation based on bioradar technology. Methods Based on the pulsed ultra-wideband bioradar platform with a center frequency of 7.29 GHz, non-contact radar data of patients with atrial fibrillation and healthy individuals were collected. The data were preprocessed, breathing and heartbeat were separated, and respiratory harmonics were suppressed. Electrocardiogram signals were synchronously collected using an electrocardiogram detector (IX-B3G). A novel method based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was proposed. The hybrid model (CNN-BiLSTM) was used to study the recognition method of atrial fibrillation by radar heartbeat signals. Results The accuracy of atrial fibrillation recognition based on the CNN-BiLSTM model was 92.16%, the precision was 88.04%, the recall rate was 85.67%, and the F1 score was 0.8683. Conclusion The research method in this study can achieve non-contact long-term heart rate monitoring and atrial fibrillation identification, which is of great significance for the early detection, early intervention and early treatment of paroxysmal atrial fibrillation and the effective conservation of medical resources.

Objective To propose a denoising method for positron emission tomography (PET) images based on adaptive hybrid filtering and verify its denoising effect at different acquisition times. Methods By dynamically adjusting the filtering window size, the appropriate filtering parameters were selected to adapt to different noise levels. To verify the effectiveness of the method, PET images of 33 patients with brain tumors were used, and three different signal acquisition times (3, 5, and 7 min) were adopted as experimental samples. By comparing the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) and mean squared error (MSE) between pixels with the traditional filtering methods, the denoising effect of the adaptive hybrid filter was evaluated. Results The experimental results showed that when the signal acquisition time was 3 min, the adaptive hybrid filtering method [PSNR: (60.9569±2.0467) dB, SSIM: 0.9960±0.0015, the MAE between pixels: 6.6901±2.1756, MSE: 0.0571±0.0217] had a better denoising effect than the traditional method, and the difference was statistically significant (P<0.05). When the acquisition time was 5 min, the results of PSNR, MAE and MSE between pixels obtained by adaptive hybrid filtering [PSNR: (62.0394±2.2481) dB, SSIM: 0.9971±0.0012, the MAE between pixels: 4.7381±1.7955, MSE: 0.0450±0.0174] were superior to those of other methods, and the difference was statistically significant (P<0.05). When the acquisition time was 7 min, the PSNR, MAE and MSE values between the pixels of the adaptive hybrid filtering [PSNR: (62.7323±2.2265) dB, SSIM: 0.9976±0.0009, MAE: 3.8339±1.5475, MSE: 0.0384±0.0149] were significantly better than those of other methods, and the difference was statistically significant (P<0.05). The visual scores of adaptive hybrid filtering at the three acquisition times (3 min: 2.64±0.50; 5 min: 3.18±0.50; 7 min: 3.55±0.46) were better than those of other methods. Conclusion The adaptive hybrid filtering method can reduce noise while preserving the edges and details of the image. It is suitable for denoising PET images and demonstrates better denoising performance on images with different noise levels, making it more robust.

Objective To explore the analysis of the diagnostic patterns of deaths and patients discharged without medical advice, and to provide a methodological basis for reducing the rate of patients discharged without medical advice and ensuring the quality and safety of patients. Methods The first-page diagnostic information of patients who died and were discharged without medical advice in 2024 was selected. Factor analysis and cluster analysis were used to initially screen the diagnostic clusters, and the Apriori algorithm was utilized to construct the association network model. The model fitting effect was evaluated through KMO and Bratlett sphere tests, and the diagnostic association groups were visually presented with the aid of load diagrams, lineage diagrams and network diagrams. Results The variables were classified in combination with the magnitudes of factor loadings, and 28 diagnostic groups were obtained. Through high-frequency diagnostic cluster analysis, eight effective cluster combinations were formed for comparison. Conclusion The association network model can fit well the diagnostic association groups of deceased and uninstructed discharged patients. By frequently diagnosing the associated incidence rate, early warning for high-risk patients with multiple organ dysfunction within the hospital can be achieved, thereby enabling the prevention and control of nosocomial infection risks and optimizing the allocation of medical resources.

Objective To study the fatigue fracture fault of the shell structure of liquid metal bearing CT tube in the working process, construct the physical fault model, and carry out reliability analysis and life prediction. Methods Based on the design information of the CT spherical tube shell, combined with numerical simulation and fault physical modeling technology, a three-dimensional simulation analysis model was constructed to obtain key data input. Based on the fatigue fracture mechanism model, a fault simulation model for life assessment was established. Through finite element analysis, complex working conditions (such as high temperature, rotational shock, vacuum pressure, etc.) were simulated, and combined with the Monte Carlo method, random sampling and fitting analysis of the life distribution were completed. Results The total strain amplitude followed a normal distribution (mean 0.00340, standard deviation 0.00017). Based on the Monte Carlo simulation with 10000 samples, the fatigue life of the shell showed a lognormal distribution, with an average fatigue cycle number of 70000 times, a distribution range of 60000 to 85000 times, and a goodness of fit of 95%. Simulation verification showed that the fault physical model could effectively evaluate the fatigue life of the CT tube shell. Conclusion The fault physical model established in this study provides important theoretical support and technical basis for the reliability design, life prediction and health management of CT tube shells.

Objective To systematically analyze the yield fluctuations of the Fastlab 2 autosynthesis system in the actual production of fluorine [18F] deoxyglucose ([18F]FDG) through statistical process control (SPC), and identify the key influencing factors and put forward specific and operational quality optimization suggestions. Methods A retrospective analysis was conducted on the production data of 43 batches of [18F]FDG in the Department of Nuclear Medicine of Guizhou Provincial People’s Hospital within the recent 3 months. The production rate was evaluated by SPC using X-bar and R control charts, and the volatility was evaluated by combining the relative standard deviation (RSD). Parameter traceability (such as vacuum pressure, hydrolysis temperature, and operational compliance) was conducted for the three failed batches (with a yield of 0) and the three low-yield batches (<40%). Results The success rate of the Fastlab 2 system was 93.02%, the yield of successful batches (excluding 3 failed batches of 0 yield) ranged from 31% to 91%, the average yield was 81.6%, and the RSD was 18.64%. The X-bar chart and R chart showed that most batches were within the control limit, and the overall production process was stable. However, the yield of some batches fluctuated due to abnormal vacuum pressure (such as 190 mbar vs. standard 600 mbar), excessively high hydrolysis temperature (93 ℃ vs. standard 85-88 ℃), and operational deviations (improper installation of ferrule, failure to perform water bag inspection). The failed batches were mainly related to equipment vacuum faults (2 times) and operational errors (1 time). Conclusion The Fastlab 2 system has a good ability of automated synthesis, but the fluctuation of yield still needs attention. By optimizing the startup process confirmation of the equipment, improving the installation process of reagent card holders, strengthening the training of operators and standardizing the management of the experimental environment, the consistency of the system and the success rate of production can be effectively enhanced, providing a referenceable optimization path for similar radioactive drug production units.

Objective To evaluate the reliability and stability of the SmartPhantom CT phantom in the automatic calibration of laser positioning lamps outside the CT simulation positioning system, and to verify whether it meets the quality control requirements of high-precision radiotherapy. Methods The C-RAD HIT5 five-axis external laser positioning lamp was automatically calibrated using the SmartPhantom CT phantom. The static positioning accuracy and geometric alignment were verified respectively by the Civco-MTTG66 phantom and the LAP Wilke phantom. The movement accuracy and repeatability of the laser lamp in the X, Y, and Z directions were tested using a film ruler, and the deviation and compliance rate after calibration were recorded. Results After calibration, the alignment deviations of the laser positioning lamp in the X, Y, and Z directions were all controlled within ±1 mm, and the deviations in accuracy and repeatability tests did not exceed ±0.5 mm. The coincidence rate of all test points reached 100%. Conclusion The automatic calibration system based on the SmartPhantom CT phantom provides an efficient, accurate and stable solution for the calibration of external laser lamps in CT simulation positioning. This system can meet the clinical needs of high-precision radiotherapy and comply with the tolerance limit requirements of relevant national standards and AAPM TG66 report, which is of great significance for improving the safety and efficacy of patient treatment.

Objective To Explore the construction and application effectiveness of a refined management mode for medical consumables from the perspective of intelligent supply chains. Methods The medical consumables refined management system architecture under the intelligent supply chain was composed of base layer, access layer, data center and application layer. The base layer realized the data collection of the whole life cycle of medical consumables circulation in hospital. The access layer realized the access of various consumption-related business information systems. The data center realized deep mining and scientific utilization of massive medical consumables business data. The application layer was the business application of all kinds of data, including the medical consumables supplier evaluation, warehouse management, consumption monitoring, usage assessment, and rationality analysis. The use data of medical consumables before and after implementing the refined management mode based on the perspective of smart supply chain in the Third Xiangya Hospital, Central South University, were selected and analyzed to verify the implementation effect of the intelligent mode. Results After the application of the refined management mode, the medical consumables management liquidation process time decreased from (70.3±3.5) h to (18.1±2.1) h, inventory turnover time decreased from (38.5±3.7) d to (26.2±2.1) d; request for replenishment time decreased from (122.3±3.4) h to (23.9±7.2) h; closed-loop traceability management time decreased from (30.6±4.1) min to (6.2±2.2) min; the conformity rate of accounts increased from 95.2%±1.1% to 99.1%±0.3%; The satisfaction rate increased from 83.2%±3.1% to 98.5%±0.9% and the satisfaction of medical staff to the management mode increased from 80.6%±2.87% to 99.1%±0.3%, all differences were statistically significant (P<0.05). Conclusion Medical consumables management mode from the perspective of smart supply chain can save medical costs, improve work efficiency, improve medical quality, and improve user satisfaction. It can provide reference for medical institutions.

Objective To address the security sharing challenges posed by the increasing amount of electronic medical record data in the process of medical informatization, to address the security sharing challenges posed by the increasing amount of electronic medical record data in the process of medical informatization. Methods Firstly, a blockchain network composed of hospital nodes was constructed, and smart contracts were used to achieve permission management and log synchronization. Then, a certificateless mechanism was adopted to simplify key management, and public and private keys were dynamically allocated through the key generation center. Finally, combined with proxy re-encryption technology, the blockchain node acted as a proxy to convert the original ciphertext into ciphertext that the target user can decrypt, ensuring that the plaintext was not exposed during the data sharing process. Results The experimental results showed that the time required for the proposed scheme to perform encryption once was 1.80 ms, and the efficiency in the key generation and decryption stages was higher than other schemes, with statistical significance (P<0.05). In the key generation stage, the proposed scheme had a relatively longer time, while in the encryption stage, the proposed scheme had higher efficiency. Conclusion The scheme proposed in this article integrated blockchain and proxy re-encryption technology, ensured the secure sharing of electronic medical records among different users and institutions, effectively enhanced the security and sharing efficiency of electronic medical records, and improved the convenience and satisfaction of the public in seeking medical treatment.

Objective To establish a magnetocardiograph usability evaluation index system from the hospital perspective, aiming to support medical institutions in making magnetocardiograph selection decisions and explore the post-marketing usability evaluation of innovative medical devices. Methods A deductive reasoning technical approach was adopted. First, literature analysis, brainstorming, and expert consultation were used to formulate general usability evaluation indicators for innovative medical devices. Then, combined with the characteristics of magnetocardiographs, system analysis, on-site investigation and interviews, and Delphi method-based expert consultation were applied to establish the magnetocardiograph usability evaluation index system. Results A usability evaluation index system for innovative medical devices was established, including 4 first-level indicators, 10 second-level indicators, and 40 third-level indicators. The core of this system was the addition of “environmental adaptability” and “ease of use” indicators. A usability evaluation index system for magnetocardiographs was also established, which contained 4 first-level indicators and 20 second-level indicators after adjusting the names of some indicators and deleting 3 second-level indicators. For the Delphi expert consultation on magnetocardiograph evaluation indicators, the authority coefficient was 0.95 and the coefficient of variation was≤0.17. Conclusion The magnetocardiograph usability evaluation index system proposed in this study has certain scientificity, authority, and feasibility. It can provide valuable references for medical institutions to conduct usability evaluation when selecting magnetocardiographs.

Objective To explore the exploration and practice of a quality control management model based on distributed quality control devices. Methods By analyzing the difficulties faced in medical equipment quality control, taking the quality control of infant incubators as an example, a distributed quality control device was developed in a targeted manner. A three-level quality control management model was constructed based on this device, and a pilot application was carried out in the department of pediatrics of China-Japan Friendship Hospital. A comparative effect study was conducted with traditional quality control devices and quality control management models. Results The quality control management model based on the distributed quality control device for infant incubators could improve the efficiency of quality control work by approximately 70.76%, achieve 100% equipment coverage and 24-hour real-time daily quality control coverage per week, reduce cost input by over 50%, and standardize the quality control processes and standards. Conclusion The innovation of the quality control management model based on distributed quality control devices systematically solves the four major pain points of traditional quality control, namely low efficiency, narrow coverage, high cost, and inconsistent standards. It provides a more efficient, economical, and reliable solution for medical equipment quality control.

Objective To explore the application of failure mode and effect analysis (FMEA) and plan-do-check-action (PDCA) cycle management in the centralized management of soft endoscopes in disinfection supply centers. Methods A total of 21 sets of soft endoscopic instruments and 20 relevant staff members from Foshan Maternal and Child Health Hospital were selected as research objects. The study was divided into a control group (January 2019 to December 2020) and an observation group (January 2022 to December 2023) according to the time of management intervention. The control group implemented a conventional management model, while the observation group implemented FMEA and PDCA cycle management. Statistical methods were used to compare the differences in professional knowledge mastery, work performance, management effectiveness, and management quality of relevant staff between the two groups. Results Compared with the control group, the observation group had a higher overall mastery of professional knowledge among staff, shorter lunch overtime hours and item retrieval time, and a higher satisfaction score, with statistically significant differences (P<0.05). For soft endoscopic instruments in the observation group, the endoscopic maintenance rate and failure rate were lower, while the rate of finding endoscopes within 3 seconds and the sampling qualification rate were higher, with statistically significant differences (P<0.05). There was no statistically significant difference in packaging qualification rate between the two groups (P>0.05). Compared with the control group, the observation group had a higher replacement frequency of disinfection brushes and detergents, a higher sterilization qualification rate, a lower gauze loss rate, and a smaller number of macular spots on the endoscopic surface, with statistically significant differences (P<0.05). Conclusion For the centralized management of soft endoscopes in disinfection supply centers, the application of FMEA and PDCA cycle management can improve the professional knowledge mastery of relevant staff, optimize work performance, and enhance management effectiveness and management quality.

Objective To improve the processing efficiency of critical examination values, ensure the traceability of critical value processing information, and design a set of closed-loop management processes for medical imaging critical values, thereby achieving refined management of critical values. Methods With reference to the national standards for the functional application level 5 evaluation of electronic medical record systems and the interconnection and interoperability standardization maturity evaluation criteria, a closedloop management process for medical imaging critical values was designed based on the hospital information integration platform, integrating data from the picture archiving and communication system, hospital information system, electronic medical record system, and medical administration management system. A hierarchical review-based critical value thesaurus was established and maintained through multi-department collaboration to realize automatic identification and reminders of critical values as well as multi-channel message push. Requirements for clinical reception, disposal, and feedback were clarified; information technology was used to conduct full-process monitoring of critical value records and integrate data statistics and analysis. The business process covered four modules: medical technology examination, clinical physician, clinical nurse, and medical administration management, forming a full-process closed-loop management. Results The timely reporting rate of critical values increased from 81.10% to 98.14%, the completeness rate of registered information increased from 76.38% to 95.17%, and the qualification rate of processing opinions increased from 86.22% to 97.03%, all with statistically significant differences (P<0.05). The satisfaction of reporting physicians, clinicians, clinical nurses, and medical administrators significantly improved after process reengineering. Conclusion The closed-loop management process for medical imaging critical values standardizes critical value management through informatization, promotes the standardization and continuous improvement of management systems, and improves overall medical efficiency through technical integration and service optimization, providing strong support for medical quality and patient safety.

Objective To construct a core evaluation index system for remote quality control of magnetic resonance imaging (MRI) and apply it to empirical research on MRI remote quality control work. Methods The key elements of MRI remote quality control evaluation were systematically analyzed through literature analysis, unstructured interview, and Delphi method to construct a core evaluation index system. Data of each index in hospital MRI remote quality control from 2022 to 2023 were collected, and the evaluation was conducted using the entropy weight-based technique for order preference by similarity to ideal solution (TOPSIS). Meanwhile, the results were compared with those of clinical photo evaluation in MRI quality control. Results A core evaluation index system for MRI remote quality control was constructed, covering 4 first-level indicators (environmental parameters, radiofrequency system, magnet system, and image performance) and 15 second-level indicators. In addition, empirical research showed that the evaluation results of the entropy weight TOPSIS method were basically consistent with those of the clinical photo evaluation method (Pearson correlation coefficient ρ=0.78). Conclusion The core evaluation index system for MRI remote quality control constructed in this study has effectiveness, objectivity, and practicability. It provides new ideas and methodological tools for digital and refined quality management of medical equipment, and the popularization and application of this system is expected to play an important role in improving the management efficiency of medical equipment.

Objective To provide a regulatory basis for medical device supervision and administration departments, adopt effective mechanisms to conduct risk control and prevention of adverse events, reduce the risks of medical device use for patients and medical staff, and ensure the safety of people’s medical device use as well as life and property. Methods Based on the Shandong Provincial Medical Device Adverse Event Monitoring Special Research Platform, data analysis of adverse events was conducted to determine the research category of medical devices. The analytic hierarchy process (AHP) was used to determine the index weight of causes for adverse events of medical devices in the research category, and consistency test analysis was performed on the weights. Targeted measures were formulated and implemented from different perspectives of device supervision, production and use, followed by continuous tracking and monitoring. Results The research category was determined as urinary catheterization-related medical devices. The AHP results showed that product quality issues were the primary cause of adverse events of urinary catheterization-related medical devices. The consistency test results of the two dimensions (CR=0) all met the requirements, which were consistent with the statistical results of the provincial platform. Usage management and personnel management were key influencing factors leading to adverse events of urinary catheterization-related medical devices. After the implementation of targeted measures, the incidence of adverse events of urinary catheterization-related medical devices in medical institutions decreased significantly. Conclusion Data analysis using AHP can effectively identify the root cause indicators of medical device adverse events. Implementing targeted measures for risk control of medical device adverse events can not only promote medical institutions to improve the requirements for the performance and function of the products used, but also drive enterprises to enhance product quality, thereby effectively reducing or even avoiding the recurrence of similar adverse events.

Objective To analyze the development trends and key review and supervision points of innovative medical devices in China from 2014 to 2023, and to study the registration status of innovative medical devices in 2023, so as to provide references for registrants. Methods Based on the approval and public announcement data of the Center for Medical Device Evaluation of the National Medical Products Administration from 2014 to 2023, the overall trends were analyzed from dimensions such as product category and region. Combined with 61 public review reports in 2023, the contents of preclinical research, clinical evaluation, innovation identification and post-marketing supervision were analyzed. Results From the perspective of development trends, the number of innovative medical devices registered and marketed in the past two years accounted for 46%, among which more than 62% were concentrated in the following four fields: “13 passive implantable devices” “01 active surgical devices” “06 medical imaging devices” and “12 active implantable devices”. According to the preclinical research data of innovative medical devices in 2023, 23 innovative medical device systems conducted animal tests; clinical trials were the main clinical evaluation method, with 46 clinical evaluation reports completed by clinical trials, 8 reports completed by the combined method of “clinical trial+same-variety comparison”, and 7 reports completed by the same-variety comparison method. Conclusion The development trend of innovative medical devices is positive, with a certain degree of field concentration while gradually expanding. Registrants of innovative medical devices should focus on “risk management”, plan research and verification paths and clinical evaluation methods based on the risk-benefit assessment of products, and continuously collect scientific evidence related to product risks and benefits throughout the whole life cycle of medical devices to implement risk management.

Objective To analyze the research status, hotspots, and development trends in the field of hospital bidding and procurement in China, and to provide references and suggestions for the implementation of subsequent bidding and procurement work and related research. Methods Based on the literature in the CNKI database, CiteSpace was used to conduct bibliometric and visual analysis on the number of published papers, authors, institutions, and keywords of literatures related to hospital bidding and procurement from 2004 to 2024. Results A total of 683 valid literatures were included. The retrieval results showed that the number of published papers on bidding and procurement research generally showed an upward trend. Except for the search terms, keywords such as “medical equipment” “drugs” “management” “problems” “countermeasures” “medical consumables” “internal management” “supervision” and “informatization” appeared with high frequency. Through the analysis of specific literatures and combined with the results of keyword co-occurrence and clustering, it was found that the research hotspots of hospital bidding and procurement mainly included four aspects: research on the problems and processes of bidding and procurement of medical equipment and medical consumables, research on audit and risk management of hospital bidding and procurement, research on hospital drug volume-based procurement, and research on hospital government procurement. Conclusion The policy documents issued by the state play a guiding role in the research related to hospital bidding and procurement. Future research should focus on the internal management and risk prevention and control of hospital bidding and procurement, strengthen government procurement management, and give play to the key role of informatization and intelligent means.

Objective To investigate the clinical application value of personalized patient protocol technology (P3T) in coronary CT angiography (CCTA) for populations with high body mass index (BMI) (28-32 kg/m2 ). Methods A total of 65 patients scheduled for CCTA scanning in Capital Medical University Affiliated Beijing Friendship Hospital from June to December 2023 were prospectively enrolled, and randomly divided into a control group (n=33) and a P3T group (n=32). The control group selected the corresponding contrast agent injection flow rate according to different BMI values. The P3T group generated personalized injection protocols based on patients’ body weight through the P3T software. The two groups were compared in terms of contrast agent dosage, injection flow rate, effective dose (ED), results of subjective evaluation of image quality, and objective evaluation of image quality [including CT values of the aortic root and each segment of the coronary artery, signal to noise ratio (SNR), and contrast to noise ratio (CNR)]. Results The contrast agent dosage and injection flow rate in the P3T group were significantly lower than those in the control group (P<0.05). There were no statistically significant differences between the two groups in CT values of the aortic root and each segment of the coronary artery, SNR, CNR, and ED (P>0.05). The subjective scores of images in both groups were ≥3 points, which could meet diagnostic needs, with good consistency (Kappa=0.81). Conclusion The application of P3T technology in high BMI populations can reduce contrast agent dosage and injection flow rate while ensuring image quality, realize the personalization of injection protocols and consistency of examinations, and is clinically feasible.

Objective To explore the diagnostic value of the CT-fractional flow reserve (CT-FFR) modules of multiple artificial intelligence software for coronary artery stenosis, and to investigate the accuracy of CT-FFR, quantitative flow ratio (QFR), and their combined value in diagnosing coronary artery stenosis. Methods A retrospective analysis was conducted on 223 patients who underwent coronary angiography (CAG) and coronary CT angiography at Baotou Central Hospital from July 2023 to July 2024. With CAG results as the “gold standard”, the diagnostic efficacy of the CT-FFR modules of three software (Shukun, United Imaging, and Bodong) for coronary artery stenosis was compared. The software with the highest diagnostic efficacy was selected to evaluate the accuracy of CT-FFR, QFR, and their combination in diagnosing coronary artery stenosis. Results In terms of diagnosing coronary artery stenosis, the diagnostic accuracy of Shukun was higher than that of United Imaging and Bodong, with a statistically significant difference (P<0.05). The combined diagnostic accuracy of Shukun’s CT-FFR module and QFR was higher than that of CT-FFR or QFR alone. Conclusion Among the three software, Shukun has higher diagnostic accuracy in diagnosing coronary artery stenosis, and all three software show good consistency with CAG. The combination of QFR and CT-FFR can improve the accuracy of diagnosing coronary artery stenosis.

Objective To explore the application value of super resolution magnetic resonance imaging technology (SupMR) in rapid magnetic resonance imaging (MRI) of the pituitary gland in children. Methods A total of 26 pediatric volunteers underwent both fast-sequence and conventional-sequence MRI scans of the pituitary gland. The fast-sequence images were transmitted to the SupMR post-processing system to generate SupMR-sequence images. Two radiologists independently performed subjective evaluations on the following sequences: fast FSE-T1WI, conventional FSE-T1WI, FSE-T1WI with SupMR, fast FSE-T2WI, conventional FSE-T2WI, and FSE-T2WI with SupMR. The evaluation indicators included pituitary clarity, artifact suppression effect, and overall image quality. Peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used as objective indicators for image quality assessment. Results The subjective evaluation results of the FSE-T1WI-SupMR sequence were superior to those of the fast FSE-T1WI and conventional FSE-T1WI sequences. There was no statistically significant difference in artifact suppression effect between the conventional FSE-T1WI sequence and the FSE-T1WI-SupMR sequence (P>0.05), while the other differences were statistically significant (P<0.05). For the FSE-T2WI-SupMR sequence, its subjective evaluation was also better than that of the fast FSE-T2WI and conventional FSE-T2WI sequences. Similarly, no statistically significant difference was observed in artifact suppression effect between the conventional FSE-T2WI sequence and the FSE-T2WI-SupMR sequence (P>0.05), but the other differences were statistically significant (P<0.05). The PSNR and SSIM between the FSE-T1WI with SupMR sequence and the conventional FSE-T1WI sequence were higher than those between the fast FSE-T1WI sequence and the conventional FSE-T1WI sequence (P<0.05). The PSNR and SSIM between the FSE-T2WI with SupMR sequence and the conventional FSE-T2WI sequence were also higher than those between the fast FSE-T2WI sequence and the conventional FSE-T2WI sequence (P<0.05). Compared with the conventional FSE-T1WI sequence, the scanning time of the SupMR FSE-T1WI sequence was reduced by 49.4%; the scanning time of the SupMR FSE-T2WI sequence was reduced by 47.6% compared with that of the conventional FSE-T2WI sequence. Conclusion The SupMR-based technology can significantly improve MRI image quality, help shorten scanning time, and realize rapid MRI scanning of the pituitary gland in children.

Raman spectroscopy is a molecular spectroscopic analysis technique based on the interaction between light and matter. In recent years, with the assistance of artificial intelligence, the information contained in Raman spectroscopy data has been effectively and in-depth mined, thereby achieving significant progress in the non-invasive diagnosis of cancer. The application of Raman spectroscopy overcomes some limitations of traditional diagnostic methods, such as high invasiveness, long detection cycle, and low sensitivity in the early stage of cancer. With the development of nanomaterials, microfluidic technology, and multimodal data fusion technology, Raman spectroscopy exhibits high accuracy and specificity in cancer detection, and demonstrates great potential especially in early cancer screening and health monitoring. This paper reviewed the current application status of Raman spectroscopy in the detection of noninvasive samples (including urine, saliva, exhaled gas, and sweat), aims to promote the application of Raman spectroscopy in fields such as clinical diagnosis, disease monitoring, and health management.

Brain tumors are lesions caused by the abnormal proliferation of brain tissue cells, posing a significant threat to human life. Magnetic resonance imaging (MRI) is a typical non-invasive imaging technique that can generate high-resolution, non-destructive brain images without skull artifacts. With the continuous advancement of biomedical technology, the application of MRI in the diagnosis and treatment of brain tumors has become a key technical means to improve patient survival rates and reduce computational costs. However, there are still technical bottlenecks in the accurate segmentation of brain tumors using multimodal MRI. Traditional methods rely on manually designed features, which are not only inefficient but also susceptible to subjective influences. In contrast, deep learning has overcome this limitation through its capability of automatic feature extraction, thereby promoting the automation and precision of segmentation. This paper reviewed the technical principles, application effects, and limitations of three types of deep learning models, namely U-Net, Transformer, and segment anything model. Finally, it analyzed the challenges faced by current research and provided an outlook on future directions, aims to provide references for researchers and clinicians in the field of medical image analysis, and further enhance the accuracy and efficiency of brain tumor diagnosis.

Mild cognitive impairment (MCI) is a transitional state characterized by mild cognitive decline, serving as a precursor stage to Alzheimer’s disease (AD). It plays critical importance in the early diagnosis and prevention of AD. As a significant tool for investigating brain functional connectivity (FC), resting-state functional magnetic resonance imaging (rs-fMRI) plays a crucial role in understanding FC changes across different subtypes of MCI. This paper reviewed the application progress of rs-fMRI in revealing brain FC changes in patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). It summarized the existing differences in FC within key brain networks, including the default mode network, executive control network, and salience network, between aMCI and naMCI patients, these differences may serve as biomarkers for the early diagnosis of MCI. This paper also discussed the clinical challenges in MCI diagnosisand how rs-fMRI can be utilized to enhance diagnostic accuracy while unraveling its underlying neurobiological mechanisms, aiming to provide neuroimaging evidence for the precision diagnosis and treatment pathway of MCI.

With the development of digital and information technologies, medical imaging has evolved from electronization and networking toward the direction of big data and intelligence. Generative medical imaging is capable of generating high-resolution images of different modalities and enabling cross-modal medical image conversion. Along with the advancement of artificial intelligence (AI), generative medical imaging is expected to play a significant role in all aspects of medical imaging. This paper reviewed the development stages of digitalization and informatization of imaging data, the application modes of AI in digital imaging, and the applications of generative medical imaging. Meanwhile, it explored the entire clinical workflow of generative medical imaging as well as the problems and difficulties in data management, analyzed the future development directions and challenges faced by generative medical imaging, and emphasized the importance and urgency of standardized management for generative imaging data, aiming to promote the management of generative AI-medical imaging data so as to meet the needs of clinical diagnosis.

Disorders of consciousness (DOC) are a group of severe neurological disorders. Accurate assessment of the severity of DOC and related pathophysiological mechanisms is crucial for patients’ treatment and prognosis. Traditional imaging examinations, such as conventional magnetic resonance imaging or computed tomography, have limitations in evaluating microstructural changes and brain functional connectivity in DOC patients. Magnetic resonance 3D-T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI) can objectively reflect abnormal brain structural and functional changes, provide detailed imaging evidence for exploring the pathophysiological mechanisms of DOC, and possess unique advantages in the field of DOC assessment. This paper reviewed the latest applications of 3D-T1WI and DTI in DOC assessment, summarized the changes in brain structure and function as well as their pathophysiological mechanisms in DOC patients, and aiming to provide a reference for the diagnosis, prognosis, and treatment of DOC.

Patients with lower limb motor dysfunction face severe limitations in motor function. Partial weight-bearing training, as an effective rehabilitation method, has attracted considerable attention for its ability to specifically help patients gradually restore mobility. However, traditional partial weight-bearing rehabilitation training has limitations such as reliance on physicians’ experience and poor patient compliance. The lower limb body weight-supported walking device has effectively broken through the limitation of high dependence on physicians in traditional rehabilitation methods, providing patients with a more independent and efficient rehabilitation training platform. This paper summarized the core concepts and development history of lower limb body weightsupported walking devices. Based on the latest research results, it classified the mainstream weight-bearing reduction devices on the market and comprehensively analyzed the unique advantages and potential shortcomings of each type of device, aiming to point out the direction for future research and development as well as application, and promote the development of lower limb rehabilitation technology towards intelligent and personalized directions.

Objective To accurately diagnose the faults of nuclear magnetic resonance equipment and provide reasonable maintenance methods. Methods Big data analysis was combined with recurrent neural network (RNN) and failure mode and effect analysis (FMEA) method, and a fault maintenance technology of nuclear magnetic resonance equipment was proposed based on the combined method. To verify the validity of this technology, it was used to inspect and repair the nuclear magnetic resonance equipment in the hospital. Results When this technology was used for fault maintenance of nuclear magnetic resonance equipment, the accuracy rate of fault detection for each part of the equipment could reach over 95%, and the detection time was less than 5.0 s. After applying this maintenance technology, the operation of nuclear magnetic resonance equipment would be more stable. Moreover, when using this maintenance technology to repair equipment faults, it was found that the repair rate can reach 99.7%. After the fault was repaired, the service life was extended by 86.8%. Conclusion The nuclear magnetic resonance fault repair technology based on the RNN-FMEA method proposed in this study can improve the accuracy of fault detection, thereby enhancing the efficiency of fault repair and improving the level of medical diagnosis.